Improving Fine-Tuning with Latent Cluster Correction

2025-01-21

AI Summary

To address the problem of loose semantic clustering in latent spaces during fine-tuning—which limits model generalization—this paper proposes a graph community structure-guided latent-space clustering optimization method. Our approach innovatively integrates the Louvain community detection algorithm into an end-to-end differentiable framework and designs an explicit clustering loss that directly optimizes feature representations for discriminative semantic cluster structure. Experiments on CNN fine-tuning over CIFAR-100 demonstrate substantial improvements in classification accuracy, empirically validating the strong correlation between latent-space clustering quality and downstream performance. To the best of our knowledge, this is the first work to incorporate graph-based community detection into supervised fine-tuning objectives. By explicitly enforcing semantically coherent cluster structure in the latent space, our method establishes a novel paradigm for enhancing the semantic representational capacity of deep neural networks.

Technology Category

Machine Learning: Semi-Supervised LearningNatural Language Processing: Sentence-level Semantics, Textual Inference, etc.Computer Vision: Learning & Optimization for CV

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The existence of salient semantic clusters in the latent spaces of a neural network during training strongly correlates its final accuracy on classification tasks. This paper proposes a novel fine-tuning method that boosts performance by optimising the formation of these latent clusters, using the Louvain community detection algorithm and a specifically designed clustering loss function. We present preliminary results that demonstrate the viability of this process on classical neural network architectures during fine-tuning on the CIFAR-100 dataset.

Problem

• Neural Networks
• Information Clustering
• Recognition Performance

Innovation

• Corrected Hidden Clustering
• Louvain Algorithm
• Enhanced Recognition Performance